In modern cars, a plurality of sensors and other sources of information enable Advanced Driver Assistance Systems (ADAS) to support a driver in complex driving situations, up to high automated driving. A camera-based surround view system represents one component of an ADAS. Such a surround view system performs freespace detection in order to identify objects in the environment of the vehicle and provides this information to the ADAS.
Freespace detection in conventional surround view systems is mainly performed with structure from motion (SFM) algorithms. These algorithms only can be applied on static environmental areas. At least two or more supporting frames are required for freespace detection with SFM algorithms, which increases latency and memory consumption. Usually, SFM algorithms can only be applied on a signal from a single camera.
Accordingly, there is a need for enhanced freespace detection. In particular, there is a need for a freespace detection which can be applied on static and moving environment, and which requires reduced computational effort.